Table 1.
Summary of relationships among GBM-related genes, GBM-related miRNAs, and TFs.
Figure 1.
Computational framework for constructing the comprehensive GBM-specific miRNA-TF regulatory network and its application for identifying critical miRNA components in a given pathway.
This framework involves four main steps. 1) Data collection. We compiled glioblastoma (GBM)-related genes, GBM-related microRNAs (miRNAs) and known human transcription factors (TFs) from public databases and literature. 2) Regulation prediction. We predicted five types of regulation (TF-gene, TF-miRNA, miRNA-gene, miRNA-TF, and gene-gene coexpression) by integrating TF binding profiles, miRNA target profiles, and gene expression profiles. 3) Identification of significant feed-forward loops (FFLs). Based on the regulation data in step 2, we assembled two types of feed-forward loops (FFLs): 3-node FFLs and 4-node FFLs. 4) Construction of a GBM-specific miRNA-TF regulatory network and performing further subnetwork analyses. By merging the FFLs identified in step 3, we constructed a GBM-specific miRNA-TF regulatory network, which consists of three types of nodes and five types of edges. Furthermore, we extracted subnetworks for core pathways reported for GBM from the GBM-specific regulatory network and predicted the miRNA components involved in these pathways.
Figure 2.
A catalogue of mixed feed-forward regulatory loops (FFLs).
According to the relationship between the transcription factor (TF) and microRNA (miRNA), the mixed FFLs were classified as the TF-FFL model (the TF directly regulates the miRNA), miRNA-FFL model (the miRNA only directly regulates the TF) or composite-FFL model (the TF and the miRNA regulate each other). The relationships represented by solid lines are required while the relationships represented by dot lines are not required. B) Five types of putative regulations involved in these FFLs: miRNA-gene represents that the miRNA represses gene expression; miRNA-TF represents that the miRNA represses the TF gene expression; TF-gene represents the regulation by TF of the expression of the gene; TF-miRNA represents the regulation of TF to expression of miRNAs; and, gene-gene represents gene coexpression.
Table 2.
Summary of 3-node and 4-node feed-forward loops based on glioblastoma related data.
Figure 3.
Graphical representations of the composite glioblastoma-related miRNA-TF regulatory network and its network characteristics.
A) Graphical representation of the composite glioblastoma miRNA-TF regulatory network. The network was generated from 3-node and 4-node composite-FFL motifs. B) Degree distribution of all nodes (genes, miRNAs and TFs) in the network. The Y-axis represents the proportion of nodes with a specific degree. C–E) Three higher-order subnetworks. In each subfigure, nodes in red correspond to GBM-related miRNAs, nodes in green correspond to GBM-related genes, and nodes in blue correspond to transcription factors. The edge colors represent different relationships: red for the repression of miRNAs to genes or TFs, blue for the regulation of TFs to genes or miRNAs, and black for the coexpression of GBM-related genes.
Table 3.
Canonical pathways overrepresented in genes involved in the composite glioblastoma-specific regulatory network.
Figure 4.
Notch-specific miRNA-TF regulatory network and its subnetworks related to GBM.
A) Notch-specific miRNA-TF regulatory network related to GBM. B) GBM gene-centered subnetwork. The subnetwork includes most of the GBM-related genes involved in the Notch-specific miRNA-TF regulatory network. C) Centered subnetwork. The subnetwork links the GBM gene-centered subnetwork and the GBM regulator-centered subnetwork. D) GBM regulator-centered subnetwork. Except for two nodes, 33 nodes are GBM-related miRNAs and human TFs. Definition of colors and shapes for nodes and edges is the same as in Figure 3.